WPS4972
Policy Research Working Paper 4972
Remittances and Natural Disasters
Ex-post Response and Contribution
to Ex-ante Preparedness
Sanket Mohapatra
George Joseph
Dilip Ratha
The World Bank
Sustainable Development Network Vice Presidency
Global Facility for Disaster Reduction and Recovery Unit
&
Development Prospects Group
Migration and Remittances Team
June 2009
Policy Research Working Paper 4972
Abstract
Macro- and micro-economic evidence suggests a international remittances seem to rely more on cash
positive role of remittances in preparing households reserves and less on selling household assets or livestock
against natural disasters and in coping with the loss to cope with drought. In Burkina Faso and Ghana,
afterwards. Analysis of cross-country macroeconomic international remittance-receiving households, especially
data shows that remittances increase in the aftermath of those receiving remittances from high-income developed
natural disasters in countries that have a larger number countries, tend to have housing built of concrete
of migrants abroad. Analysis of household survey data rather than mud and greater access to communication
in Bangladesh shows that per capita consumption was equipment, suggesting that they are better prepared
higher in remittance-receiving households than in others against natural disasters.
after the 1998 flood. Ethiopian households that receive
This paper—a joint product of the Global Facility for Disaster Reduction and Recovery (GFDRR) Unit, Sustainable
Development Network Vice Presidency, and the Migration and Remittances Team of the Development Prospects Group,
Development Economics Vice Presidency—is part of a larger effort of the GFDRR unit to disseminate the emerging findings
of the forthcoming joint World Bank-UN Assessment of the Economics of Disaster Risk Reduction. Thanks to Antonio
C. David for his contribution to the macroeconomic analysis when he was at the Development Prospects Group in early
2008. We are grateful to the reviewer, Dean Yang, for his advice and suggestions, and to Saroj Kumar Jha, Mirafe Marcos ,
S. Ramachandran, Apurva Sanghi and participants at a workshop at the World Bank for their constructive comments. Ani
Rudra Silwal provided excellent research assistance. Policy Research Working Papers are also posted on the Web at http://
econ.worldbank.org. The GFDRR team leader Apurva Sanghi can be contacted at asanghi@worldbank.org. Correspondence
regarding the paper should be addressed to Sanket Mohapatra at smohapatra2@worldbank.org.
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development
issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the
names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those
of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and
its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
Produced by the Research Support Team
Remittances and Natural Disasters: Ex-post Response
and Contribution to Ex-ante Preparedness
Sanket Mohapatra, George Joseph and Dilip Ratha
World Bank
1818 H Street, NW
Washington DC 20433
USA
Working paper version updated: October 2011
Forthcoming in Journal of Environment, Development and Sustainability
Keywords: Natural disasters, migration, remittances, poverty, coping strategies, insurance,
development finance
______________________________________________________________________________
* This paper—a joint product of the Global Facility for Disaster Reduction and Recovery (GFDRR) Unit,
Sustainable Development Network Vice Presidency, and the Migration and Remittances Team of the
Development Prospects Group, Development Economics Vice Presidency—is part of a larger effort of the
GFDRR unit to disseminate the findings a World Bank-UN Assessment of the Economics of Disaster Risk
Reduction titled ―Natural Hazards, UnNatural Disasters‖. Thanks to Antonio C. David for his contribution
to the macroeconomic analysis in the first part of the paper. We are grateful to Apurva Sanghi, Dean Yang,
Saroj Kumar Jha, Mirafe Marcos, S. Ramachandran for their constructive comments and suggestions. Ani
Rudra Silwal provided excellent research assistance. Correspondence regarding the paper should be
addressed to Sanket Mohapatra at smohapatra2@worldbank.org.
Remittances and Natural Disasters: Ex-post Response and
Contribution to Ex-ante Preparedness
1. Introduction
The literature suggests that migrant remittance flows increase in the aftermath of natural
disasters, macroeconomic or financial crises, and act as a safety net for households that
have migrants abroad (World Bank 2006).1 While there is anecdotal evidence and a
number of case studies on this phenomenon, there is little empirical evaluation of the
relationship between remittances and natural disasters (see next section for literature
survey). In this paper we examine three inter-related questions: (1) How do remittances
respond ex-post to natural disasters? (2) Do remittances help recipient households to
maintain consumption expenditure in the aftermath of disasters? (3) Are remittance-
receiving households ex-ante better prepared for disasters such as earthquakes and
floods?
We use cross-country macroeconomic data to examine the ex-post response of
migrant remittances to natural disasters for a large sample of developing countries,
income groups and geographical regions to examine the hypothesis that remittances
respond in a countercyclical (compensatory) manner to natural disasters in the recipient
economies.
This paper also relies on micro-level household survey data for several
developing countries (Bangladesh, Burkina Faso, Ethiopia and Ghana) to understand
how remittances sent by migrants residing in high-income and developing countries
contribute to ex-post disaster relief for the affected households, and to ex-ante
preparedness against future natural disasters.
To briefly summarize the results based on the different hypotheses tested for the
cross-country data and the household surveys from four countries, we find the following.
First, remittances increase in response to natural disasters in countries that have a larger
emigrant stock as a share of the home country population. Second, in the period after a
flood in Bangladesh in 1998, per capita household consumption was higher for
households that receive remittances, even after controlling for the possibility that these
households may be self-selected. Third, international remittance-receiving households in
Burkina Faso and Ghana, especially those that receive remittances from high-income
1
There are about 200 million international migrants. A large share of these international migrants or about
156 million people are from developing countries (Ratha and Shaw 2007). Migrants from developing
countries sent home an estimated $305 billion in officially recorded remittances in 2008, with these flows
larger than official aid and foreign direct investment in many developing countries.
2
OECD countries, have housing built of concrete rather than mud and have greater access
to communications, which can help in coping during natural disasters. Finally, Ethiopian
households that receive international remittances tend to rely more on their own cash
reserves during shocks to food security, and less on selling productive assets such as
household assets or livestock.
The rest of the paper is organized as follows. The next section reviews the
literature on natural disasters, migration and remittances. Section 3 presents cross-country
analysis on the ex-post response of remittances to natural disasters. In section 4, we
explore using household survey data to analyze ex-post responses and ex-ante
preparedness. Section 4.1 considers how remittances to Bangladesh helped households in
maintaining consumption after a severe flood (a rapid-onset but predictable disaster) in
1998. Section 4.2 considers for Burkina Faso and Ghana whether remittance-receiving
households are ex-ante better prepared for disasters such as earthquakes and landslides.
This section provides an analysis of how recipient households often use remittances for
investment in stronger housing and improving access to communication, which can help
in reducing vulnerability to natural disasters.2 Section 4.3 explores the coping strategies
used by remittance-recipient and non-recipient households in Burkina Faso with
predictable and recurrent droughts. Section 5 concludes.
2. Natural disasters, migration and remittances: Review of the
literature
This section provides a review of the response of remittances to natural disasters drawing
on the macro economic literature and household level studies. Anecdotal and case study
evidence seem to suggest that contrary to private international capital flows (which are
usually procyclical), remittance flows increase or remain stable after the onset of large
shocks such as natural disasters, macroeconomic or financial crises and armed conflicts
(Clarke and Wallsten, 2004, World Bank, 2005 and Weiss Fagen and Bump, 2005). Yang
(2007) provides cross-country evidence on the response of international flows to
hurricanes, and concludes that for poorer countries, increased hurricane exposure is
associated with greater remittance flows. In addition, it is estimated that in the Caribbean,
a 1 percent decrease in real gross domestic product (GDP) is associated with a 3 percent
increase in migrant remittances with a two-year lag (Mishra 2005). Figure 1 and Figure 2
provide certain instances of the response of remittances to large natural disaster in
selected countries. These indicate substantial variation in the increase in remittances
2
Such income shocks may be factored in the inter-temporal consumption and remitting decisions.
3
during and after natural disasters, with a substantial increase in remittances after the
disaster in about half of those countries.
Furthermore, there is an emerging consensus in the literature that migration and
remittances are part of an overall livelihood strategy by which households try to insure
against shocks in disaster prone regions. Migration flows increased in the aftermath of
disasters as in Jamaica in 1989 after hurricane Gilbert and in Central America in 1998
after hurricane Mitch (Wisner, 2003). In El Salvador, an agricultural shock increases the
probability of migration of a household member to the United States by 24.3 percent
(Halliday 2006).3 Increased migration can lead to an increase in remittance transfers to
the households after disaster events, but with a lag (Attzs, 2008), although figures 1 and 2
suggest that it is not necessary that there would be an unambiguous increase in
remittances in all countries after natural disasters.4
Figure 1: Increase in remittances after large natural disasters (disaster costs in
constant 2000 US dollars)
Remittance as % of GDP Year before
0.04 Disaster year
0.035 Year after
0.03
0.025
0.02
0.015
0.01
0.005
0
India 1992 Bangladesh China 1999 Mexico 2005
1998
* These represent the years in which developing countries experienced the highest damages from natural
disasters in constant 2000 US$. Estimated damages due to natural disasters were $9.4 billion in India in
1992, $4.5 billion in Bangladesh in 1998, $10.4 billion in China in 1999, $6.9 billion in Mexico in 2005.
Damages are in constant 2000 US dollars.
Source: Authors’ calculations using International Emergency Disasters Database (EM-DAT) and World
Development Indicators (WDI), World Bank.
3
However, Yang (2007) shows for El Salvador that idiosyncratic shocks to the household such as death of
a household member increase the likelihood of emigration, while covariate shocks such as earthquakes,
where the entire population is affected, can even reduce emigration.
4
Furthermore, if migration and remittance decisions are undertaken as a part of the overall coping strategy
by households in disaster prone regions, we may not necessarily observe a marked increase in remittances
in the wake of slow onset disaster event such as drought since remittances are factored into the inter-
temporal consumption decisions and will not change much unless there is an idiosyncratic shock.
4
Figure 2: Increase in remittances after large natural disasters (disaster costs as
share of GDP)
Remittance as % of GDP
0.28 Year before
Disaster year
0.23
Year after
0.18
0.13
0.08
0.03
-0.02 El Salvador 1986 Honduras 1998 Guyana 2004 Jamaica 2004
* These represent the years in which developing countries experienced the high damages as a share of GDP
from natural disasters. Damages due to natural disasters were 0.04 percent of GDP in El Salvador in 1986,
0.08 percent of GDP in Honduras in 1998, 0.01 percent of GDP in Guyana in 2004 and 0.01 percent of
GDP in Jamaica in 2004.
Source: Authors’ calculations using International Emergency Disasters Database (EM-DAT) and World
Development Indicators (WDI), World Bank.
Migrant remittances have an important consumption-smoothing effect and can
contribute to financing household investment in concrete housing and communication
equipment to increase ex-ante preparedness and to mitigate the impact of disasters in
disaster prone areas. Several country studies using household survey data confirm the
consumption smoothing role played by remittances in recipient households (see Quartey
and Blankson 2004). Yang and Choi (2006) show for the Philippines that remittances
help to compensate for nearly 65 percent of the loss in income due to rainfall shocks.5
Evidence from small-scale surveys conducted after disasters suggest that migrant
remittances may have helped recipient households. A survey of households in four
villages in Pakistan after a devastating earthquake in 2005 reveals that migrant
remittances were important factors in disaster recovery and reconstruction (Suleri and
Savage, 2006). The authors suggest quickly restoring banking and financial services to
facilitate remittance flows. Remittance-receiving households in the Aceh region of
Indonesia were found to have recovered faster from the 2004 Tsunami though because of
immediate relief provided by migrant remittances, although remittance transfers were
adversely affected due to the disruption of financial services and informal remittance
transfer channels (Wu 2006).
5
However, it is possible that the loss of the most able household members who migrate may make it
difficult for the remaining household members to cope with shocks including natural disasters.
5
In Gonavies, the largest city in Haiti, in-kind transfers from friends and relatives
abroad, especially in the United States, after the cyclone Jeane in 2004 played an
important role in relieving the immediate distress from the devastation caused by the
cyclone (Fagan 2006).6 There was a 15 percent increase in remittances to Granada after
hurricane Ivan in 2005, which helped the households to recover from the disaster (Harvey
and Savage 2007). Increased remittances helped to smooth household consumption and
compensate for the loss of assets after an earthquake in El Salvador in 2001 (Halliday
2006).
There is increasing emphasis in the policy debates on measures that can reduce
the ex-ante vulnerability to natural disasters.7 In disaster prone regions or countries, ex-
ante actions taken by households with migrants (community and the government) in
preparation for a possible disaster can substantially reduce the loss of human life and
vulnerability in the aftermath of the disaster. For example, programs to reduce the impact
on livelihoods have been introduced in countries such as Jamaica that face recurrent
devastating cyclones.8
However, although there is substantial evidence of how remittances sent by
migrants abroad contribute to ex-post responses, there is little evidence of how
remittances can facilitate ex-ante preparedness that reduces the extent of damages in the
event of a natural disaster.9 For example, remittances can contribute to disaster
preparedness by households by making resources available for investments in home
improvements so as to increase their disaster resilience. Collective remittance incomes
and diaspora contributions can be channelized to augment the efforts of the government
and international organizations.
6
In-kind remittances, especially from domestic migrants, are important in many countries, but there is very
little reliable data on these. The reported values of remittances from the household surveys include in-kind
remittances to some extent.
7
The Hyogo framework (www.unisdr.org/eng/hfa/hfa.htm) recognizes the importance of integrating
disaster concerns in the larger context of development and vulnerability reduction.
8
For example, these include green houses for horticulture that can be easily disassembled and reassembled
before and after hurricanes (UN News Center ―To Succeed, Disaster Management Strategies Must Target,
Reduce Inequalities, Vulnerabilities Faced By Poor, UN Economic and Social Council told.‖ 16 July, 2008
(http://www.un.org/News/Press/docs/2008/ecosoc6363.doc.htm)).
9
There is some evidence from a related literature on household coping strategies that receiving additional
income may reduce ex-ante vulnerability. Udry (1994) finds for a sample of rural households in northern
Nigeria that households facing increased weather variability deplete grain inventories at a slower rate to
cope with the possibility of income shocks due to weather fluctuations. In a similar work, Paxson (1992)
finds for a sample of rural farmers in Thailand that farm households experiencing rainfall shocks save a
significantly larger portion of transitory agricultural income in order smooth consumption from income
fluctuations. In another study, Rosenzweig and Wolpin (1993) show that farmers in India are more apt to
sell bullocks when they experience income shocks.
6
3. Macroeconomic evidence of the response of remittances to natural
disasters
In this section, we empirically investigate the following question for a large sample of
developing countries and across income groups and geographical regions: Do remittances
respond in a countercyclical or compensatory manner to natural disasters in the recipient
economies?
The empirical exercise is undertaken primarily to understand whether remittances
respond to natural disaster events in home countries.
3.1 Data
The outcome variables of interest are migrant remittances to a country i in a year t. The
econometric analysis is based on estimates of remittance flows to developing countries
from the World Bank’s World Development Indicators (WDI). Data on GDP per capita
and population comes primarily from the same source. Summary statistics of the different
flows and other variables of interest are presented in table 1.
Natural disaster data on the occurrence and effects of natural disasters are from
Center for Research on the Epidemiology of Diseases (CRED), International Emergency
Disasters Database (EM-DAT).10 CRED defines a disaster as a natural situation or event
which overwhelms local capacity, necessitating a request for external assistance (Noy,
2008, EM-DAT Glossary of terms). These disasters can be grouped into several
categories, of which meteorological disasters (floods, wave surges, storms, droughts, land
slides and avalanches), climatological disasters (disasters caused due to long run or
seasonal climatic variability such as drought, extreme temperatures and wild fire) and
geophysical disasters (earthquakes, tsunamis and volcanic eruptions).
Each of these categories mentioned above are not mutually exclusive and should
be considered more as a typological classification. In our analysis, we focus primarily on
all disaster events taken together within a country in a year rather than each of them
examined separately. A reason for the focus on the total impact of all disasters in this
paper is the possibility that different regions in a country can be affected by different
types of disasters in a given year and since remittances data is available only at annual
10
The Center for Research on the Epidemiology of Diseases (CRED) has collected and made publically
available data on the occurrence and effects of natural disasters from 1900 to the present with a worldwide
coverage. The database is compiled from various sources, including UN agencies, non-governmental
organizations, insurance companies, research institutions and press agencies. The EM-DAT data is publicly
available on CRED's web site at: www.cred.be.
7
frequency at the country level, we would not be able to separate the response of
remittances for a specific disaster.
Table 1: Summary statistics for developing countries
Variable Standard
Obs. Mean deviation
Remittance as a share of GDP 3,974 3.4% 7.9%
Private debt as a share of GDP 3,976 0.7% 2.6%
Portfolio equity as a share of GDP 3,661 0.1% 0.5%
Emigrants as a share of origin country population 4,995 9.2% 12.1%
Per capita GDP (constant 2000 US$) 4,035 1,469 1,530
Number of people affected per 100,000 population 2,142 4,148 12,295
Disaster damage as a percentage of GDP 898 0.004% 0.02%
Source: Authors’ calculations using International Emergency Disasters Database (EM-DAT) and World
Development Indicators (WDI), World Bank.
We utilize reported measures of the total amount of direct damage (DDAMAGE)
and the total number of people affected (DAFFECTED) for the years 1970- 2006 for all
countries on which data is reported in EM-DAT. The literature on the macroeconomic
impact of natural disasters has used similarly aggregated variables (see Noy 2008).
3.2 Empirical strategy and estimation
This section will attempt to provide more systematic cross-country evidence using data
on all available countries on the possible existence of this ―countercyclical‖ or
compensatory effect of remittance flows in the context of natural disasters at the
aggregate level.
The cross-country regression is estimated for the following specification:
Yi,t = α + β*Yi,t-1 + γ1*Disaster variablei,t-1 + γ2*Disaster variablei,t-1
+ δ1*Disaster variablei,t-1*Emigrantstocki
+ δ2*Disaster variablei,t-1*Emigrantstocki
+ Region dummiesi + Time trend + errori,t
where Yit is the remittances as a share of GDP. The disaster variable is disaster cost as
share of GDP in the previous year, or people affected as share of population in the
previous year. We include an interaction term for the stock of emigrants and the disaster
variable in a country in a given year. Other controls include per capita GDP, region fixed
effects and time trend. We introduce lagged remittances as an additional explanatory
variable to account for the observed persistence of remittance flows over time.
8
As in several previous studies (Yang 2007), we use cross-country (panel) fixed
effects regression. The fixed effects control for unobserved country specific
heterogeneity. Our analysis differs from the previous works in that we have used a large
subsample of developing countries (129 countries) for which the data is available. Also
this is one of the first studies on the determinants of the remittance flows to explicitly
introduce emigrant stocks as a share of the home country population.
3.3 Results
The cross-country results show that remittances increase in response to disasters,
especially for countries that have larger stocks of migrants abroad. For every $1 disaster
cost, remittances would increase by $0.5 (-2.0 + 24.6*0.10) for a country where the
emigrant stock is about 10 percent of the origin country population (see table 2). In the
subsequent year, the increase would be an additional $1 (-1.97 +29.7*.10). Over a period
of two years, remittances for such a country would increase by $1.5.
Table 2: Remittances increase in response to disasters
Disaster variable
Dependent variable: Disaster People affected/
Remittances as share of GDP cost/GDP population
Disaster variable -2.00 -0.01*
Disaster variable lagged -1.97 -0.01**
Disaster variable x Emigrant
stock/origin country population 24.6 0.06***
Disaster variable (t-1)x Emigrant
stock/origin country population 29.7* 0.06
Lagged Remittances/GDP 0.81*** 0.80***
Observations 3,682 3,682
R-squared 0.87 0.88
* significant at 10%; ** significant at 5%; *** significant at 1%
Source: Authors’ estimations based on International Emergency Disasters Database (EM-DAT)
and World Development Indicators (WDI), World Bank.
Second, for a country with 10 percent emigrant stock as a share of population, for
each 1 percent of population affected by a disaster, remittances would increase by 0.5
percent of GDP contemporaneously and by another 0.5 percent in the next year. Over a
period of two years, remittances to that country would increase by 1 percent of GDP.
9
4. Analysis of the role of remittances in ex-post responses and ex-ante
preparedness using household surveys
Remittances may have a positive impact on consumption, housing and human capital
accumulation in remittance-receiving households when compared to households that do
not receive remittances. We also analyze whether receiving remittances enable
households to be better prepared for unforeseen shocks. We test the following hypotheses
using household survey data: (1) remittances are positively associated with absolute
levels of household per capita consumption; and (2) remittance-receiving households
have concrete houses and better access to communication that can reduce vulnerability to
natural disasters such as earthquakes and floods.
4.1 Data and methodology
We use household survey data for Burkina Faso (2003), Ghana (2005) and Bangladesh
(1998-99), and Ethiopia (2004). In particular for Bangladesh, we have three rounds of
data collected on households after the devastating flood of July-September 1998. We use
the nationally-representative Ghana Living Standards Survey (GLSS V) conducted in
2005, the Burkina Faso Core Welfare Indicators Questionnaire Survey conducted in
2003, and the Ethiopia Welfare Monitoring Survey in 2004.
To assess the long-term effects of remittances on current consumption, we first
have to deal with the issue of self-selection: many of the factors that determine
remittance-recipient status could determine the level of per capita household
consumption. We use propensity-score matching techniques to construct a counter-factual
group of households that don’t remittances, but are otherwise similar in observable
characteristics to that of the remittance-receiving households for Bangladesh, Ghana and
Burkina Faso (Heckman, Ichimura, and Todd, 1997, 1998). This procedure helps us to
control for the endogeneity of remittance-receiving status to a large extent on the basis of
observable characteristics of the households. The findings for Ethiopia on the differences
in coping strategies for households that receive international remittances and other
households are suggestive and do not attempt to control for endogeneity.
In the regression analysis, we include factors that determine remittance-receiving
status as follows: (1) age of the household head; (2) educational attainment as shown by
the number of household members with primary, secondary and tertiary education; (3)
physical capital such as land and other assets, (4) household’s maximum education
attainment or the highest number of years of education of any household member, (5)
current area of residence (urban or rural), (6) number of children below the age of 5, (7)
number of adult male members, and (8) regional dummies. In some specifications, we
10
include additional factors that determine per capita consumption such as whether the
household receive public assistance and more detailed asset variables.
4.2 Role of remittances in maintaining consumption after 1998 flood in Bangladesh
A devastating flood in Bangladesh in July-September 1998 covered more than
two-thirds of the country and caused 2 million metric tons of rice crop losses and
threatened the livelihoods of millions through food shortages (del Ninno et al. 2001).
Three waves of representative household surveys were conducted after a flood in 1998 in
rural Bangladesh in 7 flood-affected regions (thanas) within four to sixteen months after
the flood by the International Food Policy Research Institute (IFPRI) to understand how
households cope with the flood (see del Ninno et al. 2001). The first round was
conducted in November- December 1998, the second round in April- May 1999 and the
third round was in November- December 1999. These surveys provide information on the
pre-flood asset holding and the migration and remittance histories of households (see
annex table 1). The first round of the survey contains information on various measures of
the severity of flood at the village level, such as the depth of water in the house, number
of days water remained in the house, number of days evacuated, cost of repair and a
flood index developed by IFPRI using the above flood measures.
Of the 734 households which are available in all the three surveys, 493 were
affected by the 1998 flood. Using propensity score matching technique using the
household characteristics discussed in Section 4.2, we identified 469 households which
are comparable in terms of household characteristics and other determinants of
remittance-receiving status. Among these 469 households, around 118 or 25 percent of
households receive remittances. The latter group includes households that receive
remittances either from within Bangladesh or from other countries, since information on
specific sources is not available from the surveys.
In table 3, we examine the impact of remittances on per capita monthly household
consumption sixteen months after the flood for households in the flood affected areas.
The analysis is performed on all households comparable to remittance-receiving
households in terms of observable characteristics. We find that remittances have a
positive and significant effect on per capita monthly household consumption. Since the
average household size is 6.4, a thousand taka increase in remittances to the remittance-
recipient households in the six months prior to the survey leads to about a 156 taka (=6.4
x 24.37) increase in monthly household consumption expenditure of the average
household (including those do not receive remittances).11
11
That would imply a marginal propensity of consumption of 62% out of additional remittances (since the
estimated increase in consumption above is the average increase for the matched sample which includes
11
Table 3. Bangladesh: Impact of receiving remittances on per capita household
consumption one year after the flood after controlling for the endogeneity of
remittances for flood affected-areas
Dependent variable: Per capita monthly household consumption (takas)
(1) (2)
Average monthly remittances received by household in the last six months 24.4* 24.6*
(thousands of takas) (13.7) (13.6)
Average monthly public assistance received by household in the last six months -269.9
(thousands of takas) (509.4)
Log of pre-flood assets-consumer durables 30.9*** 31.2***
(8.2) (8.2)
Log of pre-flood assets-food stock -5.0 -4.9
(7.0) (7.0)
Log of pre-flood assets-livestock 0.7 1.0
(4.5) (4.5)
Household has electricity 183.1*** 183.7***
(59.1) (59.1)
Per capita land of household 6.5*** 6.6***
(1.3) (1.3)
Maximum years of education in household 11.2* 11.5*
(6.6) (6.6)
Number of primary educated in household -25.8** -26.4**
(11.8) (11.8)
Number of secondary educated in household 18.6 17.9
(20.5) (20.5)
Number of tertiary educated in household 1.7 0.8
(76.1) (76.0)
Number of children below age 5 in household -69.0*** -69.2***
(15.7) (15.7)
Number of males above age 15 in household 73.2*** 73.5***
(18.8) (18.7)
Number of pre-flood migrants from household -6.0 -6.1
(15.9) (15.9)
Constant 180.8 174.1
(219.7) (219.2)
Observations 469 469
R-squared 0.41 0.41
Standard errors in brackets
* significant at 10%; ** significant at 5%; *** significant at 1%
Source: Authors estimations based on household survey in Bangladesh conducted by the
International Food Policy Research Institute in 1998-99 (see del Ninno et al. 2001).
4.3 Ex-ante preparedness of remittance-receiving households for disasters in Ghana
and Burkina Faso
In this section, we explore whether households in Ghana and Burkina Faso that
receive remittances ex-ante better prepared against natural disasters compared to other
households. West Africa in general and the Sahel region in particular are characterized by
households that don’t receive any remittances). This appears to be lower than the average propensity to
consume likely because of the use of remittances for reconstruction after the flood.
12
some of the most variable climates on the world, with the predominant disasters being
droughts (Brown and Crawford 2008) and floods (Armah et al. 2010). We use the latest
available Ghana Living Standard Survey (GLSS V) 2005, to estimate the impact of
remittances on ex ante preparedness of households. Of the 8687 households in the
sample, 2181 households (25 percent) receive domestic remittances, while 541 (6.5
percent) receive remittances from OECD countries and 122 (1.5 percent) receive
remittances from African countries (see annex table 2). Since we can identify the source
of remittances, we can distinguish the differential impact of remittances from relatively
richer OECD countries and poorer African countries on the receiving households.
However endogeneity of remittance-receiving status needs to be controlled for in our
analysis. As in the previous section, we used propensity score matching to construct
comparable households on the basis of observable household characteristics.
Materials used for the construction of the house potentially reveal how prepared
households are in the event of disasters such as flood, earthquakes, cyclones and
landslides. Concrete houses are usually more disaster resilient, while houses made of mud
and bricks are more susceptible to destruction in the event of a disaster. Ghanaian
households that receive international remittances from OECD countries are more likely to
have a concrete house. Without controlling for endogeneity of the remittance-receiving
decision, 44 percent of Ghanaian households that do not receive remittances have a
concrete house. 49 percent of households that receive remittances from other African
countries have a concrete house and 77 percent of households that receive remittances
from OECD countries have a concrete house.
After controlling for endogeneity of remittance-receiving status, 77 percent of
Ghanaian households that receive remittances from OECD countries have a concrete
house versus 68 percent of comparable households that do not receive remittances (see
figure 3 and annex table 3). Of households that receive remittances from other African
countries, 49 percent have a concrete house, versus 45.3 percent of comparable
households that do not receive remittances.
As shown in figure 3, even after correcting for endogeneity of remittance-
receiving status, households that receive remittances from OECD countries and those that
receive remittances from other African countries have fewer mud houses. Similarly,
remittance-receiving households have roof made of corrugated iron sheets, cement,
concrete, asbestos, slate and roofing tiles rather than roofing material made of leaves.
13
Figure 3. Ghana: Household amenities of remittance-receiving and other households
(a) Concrete house
Before matching After matching
77% 77%
68%
Comparable
households not
44%
49% 45% 49% receiving remittance
(%)
Remittance-receiving
households (%)
No Remittances Remittances Remittances from Remittances from
remittances from Africa from OECD Africa OECD
(b) Mud house
Before matching After matching
Comparable
53% 52% 50% households not
49%
receiving remittance
30% (%)
21% 21%
Remittance-receiving
households (%)
No Remittances Remittances Remittances from Remittances from
remittances from Africa from OECD Africa OECD
(c) Concrete roof
Before matching After matching
98% 92% 98%
79% 83% 81% 83%
Comparable
households not
receiving remittance
(%)
Remittance-receiving
households (%)
No Remittances Remittances Remittances from Remittances from
remittances from Africa from OECD Africa OECD
14
(d) Electricity
Before matching After matching
80% 80%
69%
Comparable
52% 51% households not
45% 46% receiving remittance
(%)
Remittance-receiving
households (%)
No Remittances Remittances Remittances from Remittances from
remittances from Africa from OECD Africa OECD
(e) Telephone - fixed
Before matching After matching
30% 30% Comparable
28% 28%
24% households not
receiving remittance
16% 16% (%)
Remittance-receiving
households (%)
No Remittances Remittances Remittances from Remittances from
remittances from Africa from OECD Africa OECD
(f) Telephone - mobile
Before matching After matching
69% 69%
Comparable
55% households not
39% receiving remittance
39%
33% 32% (%)
Remittance-receiving
households (%)
No Remittances Remittances Remittances from Remittances from
remittances from Africa from OECD Africa OECD
Source: Authors’ estimations based on Ghana Living Standards Measurement Survey (GLSS-V) 2005.
Access to electricity and communication facilities such as fixed and mobile
phones can significantly improve information on possible disasters and anticipatory
15
precautionary measures. Ghanaian households that receive international remittances tend
to have electricity. Without controlling for endogeneity of the remittance-receiving
decision, 45 percent of households that do not receive remittances have electricity. 52
percent of households that receive remittances from other African countries have
electricity and 80 percent of households that receive remittances from OECD countries
have electricity. After controlling for endogeneity of remittance-receiving status, 80
percent of households that receive remittances from OECD countries have electricity,
versus 69 percent of comparable households that do not receive remittances. Of
households that receive remittances from other African countries, 51 percent have
electricity, versus 46 percent of comparable households that do not receive remittances.
Similarly, after controlling for endogeneity of remittance-receiving status, 28
percent of Ghanaian households that receive remittances from OECD countries have a
fixed telephone, versus 24 percent of comparable households that do not receive
remittances. Of households that receive remittances from other African countries, 30
percent have a fixed telephone, versus 16 percent of comparable households that do not
receive remittances. In the case of mobile phones, after controlling for endogeneity of
remittance-receiving status, 69 percent of households that receive remittances from
OECD countries have a mobile telephone, versus 55 percent of comparable households
that do not receive remittances. Of households that receive remittances from other
African countries, 39 percent have a mobile telephone, versus 32 percent of comparable
households that do not receive remittances.
As shown in annex table 4a, regression estimates on the matched Ghanaian
households further reveal that receiving remittances from OECD countries have a
statistically significant and positive impact on the ownership of better houses and
communication amenities. Similarly annex table 4b shows that remittances from OECD
have a negative and significant impact on having low quality houses and communication
amenities. Remittances from Africa enable households to have amenities such as
electricity and fixed and mobile phones as evident from the statistically significant
coefficients of these variables in annex table 5a. A smaller amount of remittances
received by households from migrants in Africa partly explains why these households
may not be able to make long term investments in housing (see annex tables 5a and 5b).
We use a nationally-representative household survey for Burkina Faso, the Core
Welfare Indicators Questionnaire Survey, conducted in 2003 to examine the resilience of
houses to future disasters. This survey provides information on the sources of migrant
remittances. Of the 7,339 households in the sample, 13.7 percent receive remittances
from Cote d’Ivoire, the largest intra-African destination, while 2.2 percent of households
receive remittances from France, which is the most important destination of migrants
outside Africa (see annex table 6). We used propensity score matching methods to
16
construct a comparable sample of households that don’t receive remittances, but are
otherwise similar in observable characteristics to remittance-receiving households.
We find that after controlling for endogeneity, 30 percent of Burkinabe
households receiving remittances from France have concrete houses while 25 percent of
comparable households that do not receiving remittances have concrete houses (see
figure 4 and annex tables 7 and 8). Households receiving remittance from Cote D’Ivoire
are significantly worse off than households receiving remittances from France, and are
similar to Burkinabe households that do not receive any remittances.
Figure 4. Burkina Faso: Ownership of concrete house of remittance-receiving and
other households
Before matching After matching
Comparable
30% 30% households not
25% receiving remittance
(%)
16%
10% Remittance-receiving
9% 9%
households (%)
No remittances Remittances Remittances Remittances from Remittances from
from Cote D'Ivore from France Cote D'Ivore France
Source: Authors’ estimations based on Burkina Faso Core Welfare Indicators Questionnaire Survey 2003.
4.4. Coping strategies of remittance-receiving households versus other households in
Ethiopia
Ethiopia suffers form extreme poverty and frequent shocks to food security due to
recurrent droughts, floods and other natural disasters (Webb 1993, Gray and Mueller
2011). We use the nationally-representative 2004 Welfare Monitoring Survey to examine
how remittance-dependent households manage shocks to food security. Migration and
remittances are generally understood as a part of coping mechanisms adopted by
households facing shocks to incomes and livelihoods (Block and Webb, 2001). Of the
33,302 households in the survey, the majority of households (67 percent) are located in
rural areas.
A vast majority (93 percent) of Ethiopian households who report international
remittances as their main source of income reside in urban areas. In contrast, only 14
percent of rural households report international remittances as their main source of
17
income.12 We examine whether households that depend on remittances face fewer shocks
and whether these households behave differently from other households in coping with
shocks.
Figure 5. Shocks faced by Ethiopian households
24%28% 23% 25% Non remittance receiving
households
16% Domestic remittances
11%
5% 4% 3% International remittances
Households facing Illness of Drought
food shortage household member
Source: Authors’ calculations based on Ethiopia Welfare Monitoring Survey 2004.
In Ethiopia, we find that households that depend on international remittances
report facing fewer shocks from food shortages, illness and drought compared to other
households (figure 5). The remittance-receiving households that are affected by drought
tend to mostly in rural areas. While remittance-dependent households report facing fewer
shocks in terms of illness of household members—perhaps since better nutrition is
usually associated with better health—the difference with the other households is smaller
compared to the direct shocks to food security.
Table 4. Remittance recipient households do not sell productive assets and use own
cash to cope with food shortage shocks
Households not
receiving Domestic International
remittances remittances remittances
Food Aid 42.3 55.9 0
Sale of livestock and livestock products 40.5 3.9 0
Sale of other agricultural products 18.2 3.7 0
Sale of household assets 4.1 4.6 11.5
From own cash 10.3 5.3 31.3
Others 15.6 33 48.9
Source: Authors’ calculations based on Ethiopia Welfare Monitoring Survey 2004.
12
However, among the ―urban‖ households that receive remittances, 16 percent report being engaged in
agricultural or related activities.
18
Ethiopian households that receive international remittances typically do not sell
their productive assets such as household assets (in case of urban households) or
livestock (in case of rural households) to cope with shocks related to food shortages
(table 4). These households typically rely on own cash and other means, presumably from
remittances, for coping with shocks. However, while these findings suggest a positive
role of remittances during shocks related to food shortages in Ethiopia, they should not be
treated as causal since the differences between the three sets of households could result
from differences in their initial wealth and other characteristics.
5. Conclusion
This paper has presented an analysis of how migrant remittances respond in the
aftermath of natural disasters, and whether these flows contribute to preparedness for
natural disasters such as earthquakes, droughts and floods.
Based on the analysis using the macroeconomic data and micro-data from
household surveys, the paper has the following conclusions. Remittances increase in
response to natural disasters in countries that have a larger emigrant stock as a share of
the home country population. In the period after a flood in Bangladesh in 1998, per capita
household consumption was higher for households that receive remittances, even after
controlling for the possibility that these households may be self-selected. International
remittance-receiving households in Burkina Faso and Ghana, especially those that receive
remittances from high-income OECD countries, have housing built of concrete rather
than mud and have greater access to communications, which can help in coping during
natural disasters. Ethiopian households that receive international remittances tend to rely
more on cash reserves during shocks to food security, and less on selling productive
assets such as household assets or livestock.
The macro and micro-evidence indicate a positive role of remittances in preparing
for and in coping with the consequences of natural disasters. The finding from household
surveys suggest that international remittances from high-income countries tend to be
more important in enhancing ex-ante preparedness for disasters compared to those from
other developing countries or domestic remittances. This is likely to be the case since
international remittances are usually much larger in magnitude compared to intra-regional
remittances and domestic remittances (see Mohapatra and Ratha 2011 for evidence from
Africa).
The findings also provide a role for policy. Disaster response measures could
include leveraging official assistance for tapping into the diaspora after natural disasters,
19
providing resources and assistance to embassies and migrant associations to channel
contributions after disasters, and quicker restoration of financial infrastructure and money
transfer facilities that may have been disrupted so as to facilitate uninterrupted flow of
remittances by family and friends abroad to the affected population.
20
References
Armah, Frederick A., David O. Yawson, Genesis T. Yengoh, Justice O. Odoi, and Ernest
K. A. Afrifa. 2008. ―Impact of Floods on Livelihoods and Vulnerability of Natural
Resource Dependent Communities in Northern Ghana‖. Water 2(2), pp. 120-139
Attzs, M., and W. Samuel. 2007. ―Natural Disasters and Remittances in Central America
and the Caribbean.‖ Mimeo. (available at: www//sta.uwi
edu/fss/dept/academic/documents/EC25F/Remittances_DisastersVersion1 March27.pdf)
Block S, and P. Webb. 2001. ―The Dynamics of Livelihood Diversification in Post
Famine Ethiopia.‖ Food Policy 26.
Brown, Oli, and Alec Crawford. 2008. ―Climate change: A new threat to stability in
West Africa? Evidence from Ghana and Burkina Faso.‖ African Security Review 17(3)
Institute for Security Studies.
Clarke, George, and Scott Wallsten. 2004. ―Do Remittances Protect Household in
Developing Countries against Shocks? Evidence from a Natural Disaster in Jamaica.‖
Mimeo, World Bank, Washington, DC.
del Ninno, Carlo, Paul A. Dorosh, Lisa C. Smith, and Dilip K. Roy. 2011. ―The 1998
Floods in Bangladesh: Disaster Impacts, Household Coping Strategies, and Response.‖
Research Report 122, International Food Policy Research Institute, Washington DC.
EM-DAT: The OFDA/CRED International Disaster Database, Université Catholique de
Louvain, Brussels, Belgium. (Available at www.em-dat.net)
Gray, Clark, and Valerie Mueller. 2011. ―Drought and Population Mobility in Rural
Ethiopia.‖ World Development (forthcoming)
Harvey, Paul, and Kevin Savage. 2007. ―Remittances During Crises: Implications for
Humanitarian Response.‖ HPG Briefing Paper 26, Overseas Development Institute,
London, UK. (Available at http://www.odi.org.uk/hpg/papers/hpgbrief26.pdf)
Heckman, J., Ichimura, H., Todd, P. 1997. ―Matching as an Econometric Evaluation
Estimator: Evidence from Evaluating a Job Training program.‖ Review of Economic
Studies, Vol. 64 No.4,
_________. 1998. ―Characterizing Selection Bias Using Experimental Data.‖
Econometrica, Vol. 66, September.
Halliday, Timothy. 2006. ―Migration, Risk, and Liquidity Constraints in El Salvador.‖
Economic Development and Cultural Change 54(4), pp. 893-925.
Mishra, Prachi. 2005. ―Macroeconomic Impact of Remittances in the Caribbean.‖
International Monetary Fund, Washington, DC.
21
Mohapatra, Sanket, and Dilip Ratha (ed.). 2011. Remittance Markets in Africa, World
Bank Publications, Washington DC.
Noy, Ilan. 2008. ―The Macroeconomic Consequences of Disasters.‖ Journal of
Development Economics 88(2), 221-231. March.
Quartey, Peter and Blankson, Theresa. 2004. ―Do Migrant Remittances Minimize the
Impact of Macro-volatility on the Poor in Ghana.‖ Report prepared for the Global
Development Network, December, International Monetary Fund.
Rosenbaum, Paul and Donald Rubin. 1983. ―The Central Role of the Propensity Score in
Observational Studies for Causal Effects.‖ Biometrika 70, 1983, 41-55.
Skidmore, M., and Toya, H., 2002. ―Do Natural Disasters Promote Long-Run Growth?‖
Economic Inquiry 40 (4), 664–687.
Suleri, Abid Qaiyum, and Kevin Savage (2006). ―Remittances in Crisis: A Case Study of
Pakistan.‖ Overseas Development Institute, London, UK (available at
http://www.odi.org.uk/hpg/papers/BGPaper_RemittancesPakistan.pdf)
Tol, R. and Leek, F., 1999. Economic analysis of natural disasters. In: Downing, T.,
Olsthoorn, A., Tol, R. (Eds.), Climate Change and Risk. Routledge, London, pp. 308–
327.
Weiss-Fagan, Patricia. 2006. ―Remittances in Crisis: A Haiti Case Study.‖ Overseas
Development Institute, London, UK (available at
http://www.odi.org.uk/hpg/papers/BG_Haiti_remittances.pdf)
Weiss-Fagen, Patricia and Bump Micah N. 2005. ―Remittances in Conflict and Crises:
How Remittances Sustain Livelihoods in War, Crises, and Transitions to Peace.‖ The
Security-Development Nexus Program Policy Paper, International Peace Academy, New
York: NY.
Wisner, B. 2003. ―Sustainable Suffering? Reflections on Development and Disaster
Vulnerability in the Post-Johannesburg World.‖ Regional Development Dialogue, 24
(1): 135-48.
Wu, Treena. 2006. ―The Role of Remittances in Crisis. An Aceh Research Study.‖
Overseas Development Institute, London, UK (available at
http://www.odi.org.uk/hpg/papers/BG_Remittances_Aceh.pdf)
World Bank. 2006. Global Economic Prospects: Economic Implications of Remittances
and Migration, World Bank: Washington DC.
22
Yang, Dean 2007. ―Coping with Disaster: The Impact of Hurricanes on International
Financial Flows, 1970-2002.‖ Mimeo, Department of Economics, University of
Michigan, Ann-Harbor.
Yang, Dean and HwaJung Choi. 2007. ―Are Remittances Insurance? Evidence from
Rainfall Shocks in the Philippines.‖ World Bank Economic Review 21(2), 219-48.
Webb, P., 1993. ―Coping with drought and food insecurity in Ethiopia.‖ Disasters 17(1),
pp. 33–47.
23
Annex table 1. Bangladesh: Summary statistics of households affected by flood in 1998
Households Households not
receiving receiving
remittances remittances
Flood Measures
Flood measure -depth of water in the house 2.66 2.56
Flood measure-number of days of flooding 37.77 37.9
Flood measure - cost of repair 771.9 856.7
Flood measure -number of days of evacuation 9.13 10.3
Flood measure - village level food index 2.15 2.04
Household Characteristics
Log of assets -consumer durables 7.37 7.27
Log of assets -food stock 0.71 1.17
Log of assets -livestock 5.81 5.93
Has electricity 0.10 0.06
Per capita land of households 11.3 8.37
Maximum years of education in households 6.92 4.78
Number of primary educated 1.82 1.65
Number of secondary educated 1.53 0.73
Number of tertiary educated 0.08 0.03
Number of children below age 5 0.81 0.97
Number of males above age 15 1.57 1.37
Number of pre flood migrants 0.75 0.44
Received public assistance in the last six months 0.09 0.13
Amount of remittances received in the last six months 8,730 0.00
Amount of public assistance received in the last six months 40.03 59.7
Number of households 88 405
24
Annex table 2. Ghana: Summary statistics of households
Households Households
Households
Households receiving receiving
receiving
not receiving remittances remittances
domestic
remittances from OECD from African
remittances
countries countries
Housing amenities
Concrete house (%) 44.1 77.4 49.2 36.7
Mud house (%) 53.3 20.6 49.2 62.0
House – other materials (%) 2.62 2.00 1.59 1.31
Roof – concrete, iron, tiles (%) 79.2 98.0 83.3 80.6
Electricity (%) 45.2 80.0 51.6 40.1
Telephone – fixed (%) 15.7 28.4 30.2 16.1
Telephone – mobile (%) 33.4 68.7 38.9 28.3
Household characteristics
Urban (%) 41.9 76.0 36.5 33.3
Years of education of the household head 4.42 7.84 5.39 4.50
Household size 4.32 3.56 3.96 4.05
Age of the household head 43.5 47.4 50.4 49.7
Number of children below age 5 0.71 0.41 0.52 0.63
Number of males above age 15 0.98 0.66 0.87 0.90
Number of primary educated 0.46 0.42 0.62 0.43
Number of secondary educated 0.85 1.23 0.67 0.68
Number of tertiary educated 0.08 0.22 0.06 0.05
Number of technical educated 0.12 0.26 0.08 0.07
Log of consumption expenditure 16.5 17.0 17.5 16.0
Number of observations 5,835 549 126 2,284
25
Annex table 3. Ghana: Propensity score estimates of the remittance-receiving status on the
probability of having assets – comparisons between pairs of matched groups
Comparable
Remittance
households not
receiving t-statistics
receiving
households
remittances
Households receiving remittances from From OECD
OECD countries countries
Concrete house (%) 77 68 4.55
Mud house (%) 21 30 -4.31
House – other materials (%) 2 2 -1.02
Roof – concrete, iron, tiles (%) 98 92 5.31
Electricity (%) 80 69 5.11
Telephone – fixed (%) 28 24 2.16
Telephone – mobile (%) 69 55 6.26
Households receiving remittances from From African
African countries countries
Concrete house (%) 49 45 0.76
Mud house (%) 50 52 -0.51
House – other materials (%) 2 3 -0.97
Roof – concrete, iron, tiles (%) 83 81 0.54
Electricity (%) 51 46 1.16
Telephone – fixed (%) 30 16 3.53
Telephone – mobile (%) 39 32 1.61
26
Annex table 4a. Impact of receiving remittances on housing amenities of households
receiving remittances from OECD countries: Probit regression for Ghana
Roof-
Concrete Telephone Telephone
Dependent variable concrete, Electricity
house - fixed - mobile
iron, tiles
Remittance-receiving status 0.20** 0.52*** 0.29*** 0.12* 0.43***
(0.08) (0.16) (0.08) (0.07) (0.07)
Urban 0.52*** 0.66*** 1.22*** 1.33*** 0.75***
(0.09) (0.09) (0.09) (0.09) (0.09)
Years of education of the household head 0.02 0.03 0.04*** -0.01 0.02
(0.01) (0.02) (0.01) (0.01) (0.01)
Years of education of the head, squared 0.00 0.00 0.00 0.00 0.00
(0.00) (0.00) (0.00) (0.00) (0.00)
Household size -0.14*** -0.10*** -0.20*** -0.05** -0.11***
(0.02) (0.02) (0.02) (0.02) (0.02)
Age of the household head 0.01 -0.01 -0.02*** 0.00 0.00
(0.01) (0.01) (0.01) (0.01) (0.01)
Age of the household head, squared 0.00 0.00 0.00*** 0.00 0.00
(0.00) (0.00) (0.00) (0.00) (0.00)
Number of children below age 5 0.06* -0.09** 0.11*** -0.01 0.01
(0.03) (0.03) (0.03) (0.04) (0.03)
Number of males above age 15 0.04* 0.11*** 0.10*** 0.00 0.03
(0.03) (0.03) (0.03) (0.03) (0.03)
Number of primary educated 0.08** 0.16*** 0.16*** 0.07** 0.12***
(0.03) (0.04) (0.03) (0.03) (0.03)
Number of secondary educated 0.22*** 0.30*** 0.30*** 0.06** 0.23***
(0.02) (0.04) (0.03) (0.02) (0.02)
Number of tertiary educated 0.47*** 0.49** 0.53*** 0.30*** 0.72***
(0.08) (0.20) (0.08) (0.05) (0.07)
Number of technical educated 0.17*** 0.24* 0.27*** 0.11** 0.31***
(0.06) (0.13) (0.06) (0.05) (0.05)
Log of consumption expenditure 0.32*** 0.15*** 0.50*** 0.13*** 0.42***
(0.03) (0.04) (0.04) (0.04) (0.04)
Constant -6.28*** -2.10*** -8.31*** -3.52*** -7.68***
(0.57) (0.67) (0.59) (0.58) (0.58)
Observations 5,946 5,946 5,946 5,946 5,946
Robust standard errors in brackets
* significant at 10%; ** significant at 5%; *** significant at 1%
27
Annex table 4b. Impact of receiving remittances on housing amenities of households
receiving remittances from OECD countries: Probit regression for Ghana
House - other
Dependent variable Mud house Leaf roof
materials
Remittance-receiving status -0.20** -0.11 -0.59***
(0.09) (0.14) (0.14)
Urban -0.50*** -0.28 -0.65***
(0.09) (0.35) (0.09)
Years of education of the household head -0.02 0.01 -0.03*
(0.01) (0.02) (0.02)
Years of education of the head, squared 0.00 0.00 0.00
(0.00) (0.00) (0.00)
Household size 0.15*** -0.01 0.10***
(0.02) (0.03) (0.02)
Age of the household head 0.00 -0.02* 0.00
(0.01) (0.01) (0.01)
Age of the household head, squared 0.00 0.00 0.00
(0.00) (0.00) (0.00)
Number of children below age 5 -0.03 -0.16** 0.03
(0.03) (0.06) (0.03)
Number of males above age 15 -0.07** 0.12** -0.09***
(0.03) (0.06) (0.03)
Number of primary educated -0.09*** 0.07 -0.18***
(0.03) (0.06) (0.04)
Number of secondary educated -0.22*** -0.06 -0.31***
(0.03) (0.05) (0.03)
Number of tertiary educated -0.44*** -0.26 -0.62***
(0.09) (0.18) (0.19)
Number of technical educated -0.13* -0.23** -0.39***
(0.07) (0.11) (0.10)
Log of consumption expenditure -0.31*** -0.15** -0.14***
(0.04) (0.06) (0.04)
Constant 5.82*** 0.69 2.18***
(0.59) (0.97) (0.62)
Observations 5,946 5,946 5,946
Robust standard errors in brackets
* significant at 10%; ** significant at 5%; *** significant at 1%
28
Annex table 5a. Impact of receiving remittances on housing amenities for households
receiving remittances from African countries: Probit regression for Ghana
Roof-
Dependent variable concrete, Telephone - Telephone -
iron, tiles Electricity fixed mobile
Remittance-receiving status 0.05 0.31** 0.59*** 0.34**
(0.16) (0.14) (0.13) (0.14)
Urban 0.68*** 1.04*** 0.97*** 0.84***
(0.09) (0.09) (0.10) (0.09)
Years of education of the household head 0.01 0.04*** -0.01 0.01
(0.02) (0.01) (0.01) (0.01)
Years of education of the head, squared 0.00 0.00 0.00 0.00
(0.00) (0.00) (0.00) (0.00)
Household size -0.06*** -0.15*** -0.04** -0.09***
(0.01) (0.02) (0.02) (0.02)
Age of the household head -0.01 -0.02*** 0.00 0.00
(0.01) (0.01) (0.01) (0.01)
Age of the household head, squared 0.00 0.00*** 0.00 0.00
(0.00) (0.00) (0.00) (0.00)
Number of children below age 5 -0.10*** 0.09*** -0.05 0.00
(0.03) (0.03) (0.04) (0.03)
Number of males above age 15 0.11*** 0.07** 0.01 0.02
(0.03) (0.03) (0.03) (0.03)
Number of primary educated 0.14*** 0.16*** 0.07** 0.13***
(0.03) (0.03) (0.03) (0.03)
Number of secondary educated 0.30*** 0.29*** 0.06** 0.23***
(0.03) (0.03) (0.03) (0.03)
Number of tertiary educated 0.52** 0.61*** 0.33*** 0.76***
(0.24) (0.10) (0.07) (0.08)
Number of technical educated 0.29** 0.28*** 0.15*** 0.30***
(0.14) (0.07) (0.06) (0.06)
Log of consumption expenditure 0.08** 0.44*** 0.13*** 0.39***
(0.04) (0.04) (0.04) (0.04)
Constant -1.18** -7.45*** -3.59*** -7.27***
(0.59) (0.59) (0.58) (0.57)
Observations 5,783 5,783 5,783 5,783
Robust standard errors in brackets
* significant at 10%; ** significant at 5%; *** significant at 1%
29
Annex table 5b. Impact of receiving remittances on housing amenities for households
receiving remittances from African countries: Probit regression for Ghana
House-other
Dependent variable Mud House
materials Roof-leaves
Remittance-receiving status -0.13 -0.16 -0.05
(0.13) (0.30) (0.15)
Urban -1.66*** 0.57*** -1.02***
(0.10) (0.21) (0.12)
Years of education of the household head -0.02 0 -0.02
(0.01) (0.02) (0.02)
Years of education of the head, squared 0.00 0.00 0.00
(0.00) (0.00) (0.00)
Household size 0.13*** -0.01 0.06***
(0.02) (0.03) (0.01)
Age of the household head 0.00 -0.02 0.00
(0.01) (0.01) (0.01)
Age of the household head, squared 0.00 0.00 0.00
(0.00) (0.00) (0.00)
Number of children below age 5 -0.01 -0.15** 0.07**
(0.03) (0.06) (0.03)
Number of males above age 15 -0.05* 0.11** -0.10***
(0.03) (0.05) (0.03)
Number of primary educated -0.10*** 0.08 -0.15***
(0.03) (0.06) (0.03)
Number of secondary educated -0.24*** -0.05 -0.28***
(0.03) (0.05) (0.03)
Number of tertiary educated -0.61*** -0.40** -0.60***
(0.11) (0.19) (0.20)
Number of technical educated -0.21*** -0.16 -0.34***
(0.08) (0.11) (0.10)
Log of consumption expenditure -0.30*** -0.12** -0.09**
(0.04) (0.06) (0.03)
Constant 5.60*** 0.27 1.35**
(0.58) (0.92) (0.56)
Observations 5,783 5,783 5,783
Robust standard errors in brackets
* significant at 10%; ** significant at 5%; *** significant at 1%
30
Annex table 6. Burkina Faso: Summary statistics
Households Households
Households
receiving receiving
not receiving
remittances remittances from
remittances
from France Cote D’ivoire
Housing variables
Concrete house (%) 30.4 15.6 8.9
Mud, mud, brick house (%) 68.3 80.2 90.1
Has phone (%) 11.2 14.1 16.4
Household characteristics
Urban (%) 43.5 30.8 13.8
age of household head 44.4 43.2 48.2
years of education of household head 3.66 2.34 1.05
Asset index of the households 1.88 1.36 1.18
Number of males above the age of 15 1.66 1.65 1.72
Number of children below the age of 5 0.93 1.24 1.36
Number of primary educated in the households 1.12 0.94 0.85
Number of secondary educated in the households 0.64 0.41 0.20
Number of tertiary educated in the households 0.16 0.05 0.02
Number of households 161 6,169 1,009
31
Annex table 7. Burkina Faso: Propensity score estimates of remittance-receiving status on
the likelihood of having concrete house – comparisons between pairs of groups
Comparable
Remittance households
receiving not receiving
% of households with concrete walls households remittances t-statistics
Households receiving remittances from France
1.4
countries 30 25
Households receiving remittances from African
-1.4
countries 9 10
Households receiving domestic remittances 18 17 0.4
32
Annex table 8. Impact of receiving remittance on ownership of houses with concrete walls:
Probit regression for Burkinabe households receiving remittances from African countries
Concrete House
Household receives remittances (dummy) 0.45***
(0.10)
Urban 2.00***
(0.09)
Age of household head -0.01*
(0.00)
Years of education of household head -0.01*
(0.01)
Asset index of the households 1.26***
(0.05)
Number of males above the age of 15 -0.02
(0.03)
Number of children below the age of 5 0
(0.03)
Number of primary educated in the households 0.01
(0.02)
Number of secondary educated in the households -0.01
(0.03)
Number of tertiary educated in the households -0.17*
(0.10)
Constant -4.64***
(0.19)
Observations 7,169
Robust standard errors in brackets
* significant at 10%; ** significant at 5%; *** significant at 1%
33